Risk-Aware Path Planning

Latest papers

26 papers6 papers in the last four weeks, up 20% on the four weeks before. 0.0% of all new papers.

When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving
Nov 1, 2026Youran Wang, Devodita Chakravarty, Yiwei LyuDual Control Autonomous Driving Planning
the Departments of Computer Sciences and Mathematics, University of Wisconsin–Madison, USA. · the Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India. · the Department of Computer Science and Engineering, Texas A&M University, USA.
SAFARI: Semantic- And Frontier-driven Autonomous Risk Inspection
Oct 30, 2026Ugur Kafkasyali, Mihnea Toader, Julia Richter +3Autonomous Robot Exploration Robotic Object Search
Robotic Systems Lab, Swiss Federal Institute of Technology (ETH Zürich), 8092 Zürich, Switzerland
Don't Risk It!: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation
Oct 29, 2026Mathai Mathew Pulicken, Laksh Nanwani, Basant Sharma +3Robot Navigation Safe Robot Navigation
Robotics Research Center, IIIT Hyderabad, India. · University of Tartu, Estonia. · Inria, Université de Lorraine, France.
Balancing Mobility Efficiency and Environmental Exposure During Mega Events: A Multi-agent Routing Framework
Oct 26, 2026Xinyue Liu, Qiumeng Li, Suhong Zhou +3Risk-Aware Path Planning Vehicle Routing
Thrust of Urban Governance and Design, Society Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · Thrust of Intelligent Transportation, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China +2
TravSUITE: Traversability via Self-Supervised, Uncertainty-Aware IRL and Terrain Estimation
Oct 23, 2026Samuel Triest, Amirreza Shaban, David D. Fan +2Robot Traversability Estimation Off-Road Robot Navigation
Robotics Institute, Carnegie Mellon University · Field AI
ProTrav: Probabilistic Traversability with Elevation Uncertainty Propagation for Risk-aware Off-road Navigation
Oct 22, 2026Chuanlin Zuo, Miloš Prágr, Arun Kumar Singh +2Robot Traversability Estimation Off-Road Robot Navigation
Faculty of Engineering and Natural Sciences, Tampere University, Finland. · Institute of Technology, University of Tartu, Estonia. · AI and ML Department, Volvo Autonomous Solutions, Sweden.
Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation
Oct 7, 2026Richie R. Suganda, Bin HuRisk-Aware Path Planning Safe Motion Planning
Department of Electrical Engineering and Computer University of Houston Houston, TX 77004, USA · Department of Engineering Technology University of Houston Houston, TX 77004, USA
Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form
Oct 5, 2026Aleš Kučera, Karel ZimmermannUncertainty Quantification Safe Motion Planning
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
Foundation-Model-Guided Topology-Aware Semantic Risk Fields for Manipulation
Sep 29, 2026Giung Lee, Weihang Guo, Lydia E. KavrakiRobot Safety Robotic Manipulation
Department of Computer Science, Rice University, Houston, TX, USA
Risk-Aware Motion Planning and Control under Unknown Dynamics with Hybrid Observations
Sep 20, 2026Zhiquan Zhang, Melkior OrnikRobotic Control Safe Motion Planning
University of Illinois Urbana-Champaign, IL, 61801 USA
Time-Efficient Iterative Learning Planning for Safety-Critical Dynamic Obstacle Avoidance
Sep 17, 2026Zhiyi Chen, Shuli Lv, Chen Min +3Control Barrier Functions Safe Robot Navigation
National Key Laboratory of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China · School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
When Information is Worth the Risk: Behavioral Valuation for Hazardous Robotic Exploration
Sep 9, 2026Alkesh K. Srivastava, Aamodh Suresh, Carlos Nieto-Granda +1Risk-Aware Path Planning Value of Information
Department of Mechanical Engineering, Temple University, Philadelphia, PA 19122, USA · U.S. DEVCOM Army Research Laboratory, Adelphi, MD 20783, USA · Lobster Robotics, The Hague, South Holland, Netherlands
Risk-Aware Kinodynamic Motion Planning Under Uncertainty For Safe Navigation on Planetary Environments
Aug 11, 2026Sachin Sunil Kelkar, Tanmay Dokania, Yashwanth Kumar NakkaSampling-Based Motion Planning Kinodynamic Motion Planning
Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, Atlanta, GA, 30332 USA.
MPPI Planning with Gaussian-Based Human Cost Function for Social Navigation
Aug 8, 2026Chinmay MundaneSocial Robot Navigation Safe Motion Planning
VJTI, India
Stochastic Multi-Objective Kinodynamic Planning Against Adversaries
Jul 21, 2026Thomas Marshall Vielmetti, Daniel Cherenson, Dimitra PanagouConstrained Motion Planning Kinodynamic Motion Planning
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · Department of Robotics and Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA
MOAR Planner: Multi-Objective and Adaptive Risk-Aware Path Planning for Infrastructure Inspection with a UAV
Jun 29, 2026Louis Petit, Alexis Lussier DesbiensUAV Navigation Risk-Aware Path Planning
the Createk Design Lab, University of Sherbrooke, Sherbrooke, J1K 2R1, Canada
Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
Jun 2, 2026Amirhossein Mollaei Khass, Vivek Pandey, Guangyi Liu +3Next-Best-View Planning Multi-Robot Exploration
Department of Mechanical Engineering and Mechanics, Lehigh University · Amazon Robotics
From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction
May 30, 2026Xinyi Ning, Zilin Bian, Dachuan Zuo +2Autonomous Driving Risk-Aware Path Planning
Department of Civil and Urban Engineering, New York University. · Department of Civil Engineering Technology and Environmental Management Safety, Rochester Institute of Technology.
Provably Safe Motion Planning Under Unknown Disturbances
May 26, 2026Ibon Gracia, Qi Heng Ho, Luca Laurenti +1Sampling-Based Motion Planning Chance-Constrained Motion Planning
Department of Aerospace Engineering Sciences at the University of Colorado Boulder, CO, USA · Department of Aerospace and Ocean Engineering, Virginia Tech, VA, USA · Delft University of Technology, the Netherlands, and the Italian Institute of Artificial Intelligence, Italy
RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
May 25, 2026Zhengye Han, Quanyan ZhuSafe Robot Navigation Risk-Aware Path Planning
Department of Electrical and Computer Engineering New York University
MC-Risk: Multi-Component Risk Fields for Risk Identification and Motion Planning
May 20, 2026Maximilian Link, Yingjie Xu, Yingbai Hu +1Safe Motion Planning Autonomous Driving Planning
Technical University of Munich · Munich, Germany · The Chinese University of Hong Kong +3
Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
May 7, 2026Aditya Sai Ellendula, Yi Wang, Chandrajit BajajRisk-Sensitive RL Dynamical Systems
Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA · Oden Institute, University of Texas at Austin, Austin, TX 78712, USA
Semantic Risk-Aware Heuristic Planning for Robotic Navigation in Dynamic Environments: An LLM-Inspired Approach
May 4, 2026Hamza Ahmed Durrani, Rafay Suleman DurraniSafe Robot Navigation Robot Safety
Sejong University, Computer Science Engineering · Technische Universität Ilmenau, Computer Engineering
Multi-robot Graph Traversal with Support Coordination under Stochastically Moving Adversaries
Mar 16, 2026Manshi Limbu, Xuan Wang, Gregory J. Stein +2Multi-Robot Motion Planning Risk-Aware Path Planning
George Mason University
Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty
Mar 4, 2026Tichakorn WongpiromsarnRisk-Aware Path Planning Decision-Making under Uncertainty
Department of Computer Science, Iowa State University, Ames, IA 50010
HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving
Feb 1, 2026Weizhe Tang, Junwei You, Jiaxi Liu +7Long-Tail Autonomous Driving VLMs for Autonomous Driving
Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI 53711 USA

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When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving

Youran Wang, Devodita Chakravarty, Yiwei Lyu

the Departments of Computer Sciences and Mathematics, University of Wisconsin–Madison, USA. · the Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India. · the Department of Computer Science and Engineering, Texas A&M University, USA.

Dual Control Autonomous Driving Planning Risk-Aware Path Planning Adaptive Control Adaptive MPC Belief-Space Planning+1

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Safe navigation in interactive traffic requires autonomous vehicles to reason about unobservable human characteristics and the reciprocal nature of multi-agent interactions. Traditional architectures often decouple prediction from planning, leading to the "Frozen Robot Problem" or unsafe maneuvers under high ambiguity because they treat risk assessment and information acquisition as independent objectives. We argue that the notion of safety should not remain static but must instead dynamically co-evolve with the system's internal confidence: the robot should actively clarify ambiguity when necessary while also recognizing the diminishing returns of information, and naturally transition toward task execution. We introduce a unified belief-space Model Predictive Control (MPC) framework that couples multistep information planning with a clarity-adaptive Conditional Value-at-Risk (CVaR) barrier mechanism. By treating clarity, a scalar informational state, as an explicit planning variable, the controller co-designs its probing strategy with its safety specification, tightening conservatism under high ambiguity and relaxing constraints as confidence improves. We further provide a performance analysis showing that the cost of information seeking and the expected planning-cost gap to a controller that knows the true human characteristic are bounded by residual ambiguity and solver error. We evaluate the framework in lane-merging and unsignalized-intersection scenarios against representative interactive-planning baselines. Across 5,000 simulations, the proposed method achieves the highest success rates (94.6% and 93.2%) and the lowest collision rates (1.2% and 1.4%) among the compared methods, providing a principled foundation for robust decision-making in human-centric environments.